MultiQC Report

General Statistics

Showing ⁰/₁₀₅ rows and ¹⁹/₃₄ columns.

Sample Name	dupInt	Duplication	5'-3' bias	M Aligned	Proper Pairs	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Reads	Reads mapped	% Reads mapped	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Dups	GC	Avg len	Median len	Failed	Seqs	Trimmed bases	Dups	GC	Avg len	Median len	Failed	Seqs
101_F3	0.1	47.1%	1.00	22.5M	47.4%	0.58%	4.0M	45.1M	100.0%	100.0%	0.3%	45.1M	49.1M	49.1M	100.0%	29.6M	22.6M	76.2%	21.3M	72.0%	1.2M
101_F3_1																						58.4%	48.0%	150bp	150bp	45%	29.6M	5.2%	69.6%	48.0%	140bp	147bp	27%	29.6M
101_F3_2																						54.3%	49.0%	150bp	150bp	36%	29.6M	5.3%	64.5%	49.0%	140bp	147bp	9%	29.6M
114_F4	0.1	49.6%	0.99	17.2M	44.4%	0.51%	3.6M	34.4M	100.0%	100.0%	0.4%	34.4M	37.9M	37.9M	100.0%	23.0M	17.2M	74.8%	16.1M	70.1%	1.1M
114_F4_1																						59.9%	48.0%	150bp	150bp	36%	23.0M	5.1%	69.9%	48.0%	140bp	147bp	18%	23.0M
114_F4_2																						56.2%	48.0%	150bp	150bp	36%	23.0M	5.2%	66.1%	48.0%	140bp	147bp	18%	23.0M
117_F3	0.1	49.8%	0.99	15.9M	43.3%	0.53%	4.1M	31.8M	100.0%	100.0%	0.7%	31.8M	35.8M	35.8M	100.0%	20.9M	15.9M	76.0%	14.7M	70.5%	1.2M
117_F3_1																						60.5%	47.0%	150bp	150bp	36%	20.9M	5.9%	69.9%	47.0%	139bp	147bp	18%	20.9M
117_F3_2																						57.3%	48.0%	150bp	150bp	36%	20.9M	6.0%	65.9%	48.0%	139bp	147bp	18%	20.9M
119_F3	0.1	42.3%	0.98	12.3M	52.8%	0.58%	1.6M	24.6M	100.0%	100.0%	0.3%	24.6M	26.2M	26.2M	100.0%	15.9M	12.3M	77.2%	11.8M	74.2%	0.5M
119_F3_1																						53.0%	48.0%	150bp	150bp	36%	15.9M	5.8%	63.6%	48.0%	139bp	147bp	27%	15.9M
119_F3_2																						48.7%	48.0%	150bp	150bp	27%	15.9M	5.8%	59.6%	48.0%	139bp	147bp	18%	15.9M
123_F4	0.1	39.7%	0.98	16.3M	55.1%	0.59%	2.2M	32.7M	100.0%	100.0%	0.2%	32.7M	34.8M	34.8M	100.0%	20.7M	16.3M	78.9%	15.6M	75.6%	0.7M
123_F4_1																						51.1%	49.0%	150bp	150bp	45%	20.7M	5.8%	62.8%	49.0%	139bp	147bp	27%	20.7M
123_F4_2																						48.3%	49.0%	150bp	150bp	27%	20.7M	5.8%	58.3%	49.0%	139bp	147bp	18%	20.7M
124_F4	0.1	40.7%	1.00	19.4M	55.2%	0.60%	1.9M	38.8M	100.0%	100.0%	0.1%	38.8M	40.6M	40.6M	100.0%	23.2M	19.4M	83.4%	18.8M	80.8%	0.6M
124_F4_1																						52.5%	48.0%	150bp	150bp	45%	23.3M	5.7%	64.1%	48.0%	139bp	147bp	27%	23.2M
124_F4_2																						47.8%	49.0%	150bp	150bp	27%	23.3M	5.8%	60.0%	49.0%	139bp	147bp	18%	23.2M
131_M4	0.1	43.7%	0.98	23.2M	50.3%	0.58%	4.3M	46.4M	100.0%	100.0%	0.4%	46.4M	50.7M	50.7M	100.0%	28.9M	23.2M	80.5%	22.1M	76.7%	1.1M
131_M4_1																						55.5%	49.0%	150bp	150bp	45%	28.9M	4.5%	67.0%	49.0%	141bp	147bp	27%	28.9M
131_M4_2																						51.3%	50.0%	150bp	150bp	36%	28.9M	4.6%	62.9%	50.0%	141bp	147bp	9%	28.9M
134_F5	0.1	44.4%	1.00	22.1M	49.8%	0.61%	3.9M	44.2M	100.0%	100.0%	0.4%	44.2M	48.1M	48.1M	100.0%	28.7M	22.1M	77.1%	20.9M	72.9%	1.2M
134_F5_1																						56.5%	49.0%	150bp	150bp	45%	28.7M	4.7%	67.9%	49.0%	141bp	147bp	27%	28.7M
134_F5_2																						52.6%	49.0%	150bp	150bp	45%	28.7M	4.8%	63.6%	49.0%	141bp	147bp	18%	28.7M
135_M4	0.1	39.8%	0.99	15.5M	54.2%	0.62%	2.6M	31.0M	100.0%	100.0%	0.3%	31.0M	33.6M	33.6M	100.0%	19.7M	15.5M	78.8%	14.7M	74.9%	0.8M
135_M4_1																						51.2%	49.0%	150bp	150bp	45%	19.7M	5.5%	61.4%	48.0%	140bp	147bp	27%	19.7M
135_M4_2																						48.4%	49.0%	150bp	150bp	27%	19.7M	5.6%	59.6%	49.0%	140bp	147bp	18%	19.7M
139_M4	0.1	41.7%	1.00	15.7M	53.4%	0.60%	2.0M	31.4M	100.0%	100.0%	0.2%	31.4M	33.4M	33.4M	100.0%	21.6M	15.7M	72.7%	15.0M	69.6%	0.7M
139_M4_1																						53.2%	48.0%	150bp	150bp	45%	21.6M	5.0%	64.9%	49.0%	141bp	147bp	27%	21.6M
139_M4_2																						48.6%	49.0%	150bp	150bp	27%	21.6M	5.1%	60.1%	49.0%	140bp	147bp	18%	21.6M
140_M4	0.1	42.1%	1.01	22.4M	53.3%	0.60%	2.6M	44.7M	100.0%	100.0%	0.2%	44.7M	47.4M	47.4M	100.0%	29.0M	22.4M	77.2%	21.5M	74.4%	0.8M
140_M4_1																						53.6%	48.0%	150bp	150bp	45%	29.0M	5.5%	65.8%	48.0%	140bp	147bp	27%	29.0M
140_M4_2																						48.6%	49.0%	150bp	150bp	27%	29.0M	5.5%	61.2%	49.0%	140bp	147bp	27%	29.0M
142_F5	0.1	51.2%	1.00	25.2M	43.7%	0.63%	4.3M	50.4M	100.0%	100.0%	0.2%	50.4M	54.7M	54.7M	100.0%	31.3M	25.2M	80.7%	23.8M	76.1%	1.4M
142_F5_1																						61.5%	48.0%	150bp	150bp	45%	31.3M	5.1%	71.8%	48.0%	140bp	147bp	27%	31.3M
142_F5_2																						56.1%	49.0%	150bp	150bp	45%	31.3M	5.2%	66.9%	49.0%	140bp	147bp	27%	31.3M
144_M4	0.1	46.5%	1.01	18.4M	48.9%	0.56%	2.4M	36.7M	100.0%	100.0%	0.1%	36.7M	39.2M	39.2M	100.0%	23.3M	18.4M	78.7%	17.5M	75.2%	0.8M
144_M4_1																						57.1%	48.0%	150bp	150bp	45%	23.4M	5.8%	67.5%	48.0%	139bp	147bp	27%	23.3M
144_M4_2																						53.0%	48.0%	150bp	150bp	36%	23.4M	5.9%	62.8%	48.0%	139bp	147bp	18%	23.3M
146_M4	0.1	45.4%	1.02	16.8M	50.0%	0.59%	2.2M	33.6M	100.0%	100.0%	0.2%	33.6M	35.8M	35.8M	100.0%	20.7M	16.8M	80.9%	16.0M	77.4%	0.7M
146_M4_1																						56.8%	49.0%	150bp	150bp	45%	20.8M	4.0%	67.1%	49.0%	142bp	147bp	27%	20.7M
146_M4_2																						51.9%	49.0%	150bp	150bp	27%	20.8M	4.1%	62.3%	49.0%	142bp	147bp	9%	20.7M
152_M4	0.1	48.6%	0.99	16.8M	45.9%	0.59%	3.0M	33.7M	100.0%	100.0%	0.2%	33.7M	36.7M	36.7M	100.0%	20.9M	16.8M	80.5%	15.8M	75.7%	1.0M
152_M4_1																						58.1%	49.0%	150bp	150bp	36%	20.9M	5.5%	66.7%	49.0%	140bp	147bp	18%	20.9M
152_M4_2																						56.8%	49.0%	150bp	150bp	36%	20.9M	5.6%	66.5%	49.0%	140bp	147bp	18%	20.9M
154_F5	0.1	47.8%	1.02	19.0M	47.3%	0.59%	2.9M	37.9M	100.0%	100.0%	0.3%	37.9M	40.8M	40.8M	100.0%	24.3M	19.0M	78.1%	18.1M	74.4%	0.9M
154_F5_1																						57.1%	48.0%	150bp	150bp	45%	24.3M	3.7%	68.4%	48.0%	142bp	147bp	27%	24.3M
154_F5_2																						54.6%	49.0%	150bp	150bp	36%	24.3M	3.7%	65.4%	49.0%	142bp	147bp	27%	24.3M
158_F5	0.1	48.9%	1.01	15.4M	46.1%	0.58%	2.4M	30.9M	100.0%	100.0%	0.2%	30.9M	33.2M	33.2M	100.0%	20.0M	15.4M	77.0%	14.6M	72.9%	0.8M
158_F5_1																						59.5%	48.0%	150bp	150bp	45%	20.1M	5.4%	68.8%	48.0%	140bp	147bp	27%	20.0M
158_F5_2																						55.1%	48.0%	150bp	150bp	36%	20.1M	5.5%	64.2%	48.0%	140bp	147bp	18%	20.0M
159_F5	0.1	50.4%	1.01	18.4M	44.6%	0.60%	3.0M	36.8M	100.0%	100.0%	0.2%	36.8M	39.7M	39.7M	100.0%	23.6M	18.4M	77.8%	17.4M	73.6%	1.0M
159_F5_1																						61.8%	48.0%	150bp	150bp	45%	23.7M	4.5%	70.9%	48.0%	141bp	147bp	27%	23.6M
159_F5_2																						56.6%	49.0%	150bp	150bp	36%	23.7M	4.6%	66.3%	49.0%	141bp	147bp	27%	23.6M
173_F5	0.1	53.3%	0.98	18.6M	41.1%	0.57%	3.9M	37.2M	100.0%	100.0%	0.3%	37.2M	41.2M	41.2M	100.0%	23.4M	18.6M	79.4%	17.5M	74.5%	1.2M
173_F5_1																						62.3%	49.0%	150bp	150bp	45%	23.5M	6.3%	71.9%	48.0%	139bp	147bp	27%	23.4M
173_F5_2																						60.5%	49.0%	150bp	150bp	36%	23.5M	6.4%	69.4%	49.0%	139bp	147bp	18%	23.4M
176_F5	0.1	50.8%	1.00	19.8M	43.7%	0.56%	3.8M	39.6M	100.0%	100.0%	0.3%	39.6M	43.4M	43.4M	100.0%	24.8M	19.8M	80.0%	18.6M	75.2%	1.2M
176_F5_1																						59.8%	48.0%	150bp	150bp	45%	24.8M	5.1%	70.0%	48.0%	140bp	147bp	27%	24.8M
176_F5_2																						57.7%	49.0%	150bp	150bp	36%	24.8M	5.2%	67.8%	49.0%	140bp	147bp	18%	24.8M
35_F1	0.1	51.7%	1.03	19.7M	42.7%	0.59%	3.8M	39.3M	100.0%	100.0%	0.3%	39.3M	43.2M	43.2M	100.0%	25.5M	19.7M	77.0%	18.5M	72.4%	1.2M
35_F1_1																						61.0%	49.0%	150bp	150bp	45%	25.6M	5.2%	71.0%	49.0%	140bp	147bp	27%	25.5M
35_F1_2																						56.5%	49.0%	150bp	150bp	36%	25.6M	5.3%	66.7%	49.0%	140bp	147bp	18%	25.5M
36_F1	0.1	49.2%	1.00	18.9M	45.4%	0.55%	3.4M	37.9M	100.0%	100.0%	0.4%	37.9M	41.3M	41.3M	100.0%	25.2M	18.9M	75.3%	17.9M	71.2%	1.0M
36_F1_1																						60.0%	49.0%	150bp	150bp	45%	25.2M	5.0%	70.2%	49.0%	141bp	147bp	27%	25.2M
36_F1_2																						55.2%	49.0%	150bp	150bp	36%	25.2M	5.0%	66.0%	49.0%	141bp	147bp	18%	25.2M
45_M3	0.1	51.2%	1.00	17.6M	43.8%	0.57%	3.0M	35.3M	100.0%	100.0%	0.3%	35.3M	38.3M	38.3M	100.0%	21.8M	17.7M	81.1%	16.7M	76.7%	1.0M
45_M3_1																						62.1%	48.0%	150bp	150bp	45%	21.8M	4.3%	71.2%	48.0%	142bp	147bp	27%	21.8M
45_M3_2																						57.3%	49.0%	150bp	150bp	36%	21.8M	4.4%	66.8%	49.0%	141bp	147bp	18%	21.8M
51_F1	0.1	50.3%	1.01	19.4M	43.5%	0.57%	4.3M	38.7M	100.0%	100.0%	0.5%	38.7M	43.0M	43.0M	100.0%	24.5M	19.4M	78.9%	18.1M	74.0%	1.2M
51_F1_1																						60.2%	49.0%	150bp	150bp	45%	24.6M	5.5%	70.2%	49.0%	140bp	147bp	27%	24.5M
51_F1_2																						57.0%	49.0%	150bp	150bp	36%	24.6M	5.6%	66.6%	49.0%	140bp	147bp	18%	24.5M
54_F1	0.1	55.8%	1.01	18.5M	36.9%	0.56%	6.0M	37.0M	100.0%	100.0%	0.9%	37.0M	42.9M	42.9M	100.0%	23.6M	18.5M	78.3%	16.9M	71.8%	1.5M
54_F1_1																						64.3%	49.0%	150bp	150bp	45%	23.6M	4.3%	73.7%	49.0%	142bp	147bp	36%	23.6M
54_F1_2																						62.0%	50.0%	150bp	150bp	45%	23.6M	4.5%	71.3%	50.0%	141bp	147bp	36%	23.6M
55_F1	0.1	55.4%	1.00	17.9M	36.9%	0.55%	6.2M	35.8M	100.0%	100.0%	0.9%	35.8M	42.0M	42.0M	100.0%	22.9M	17.9M	78.1%	16.3M	70.9%	1.6M
55_F1_1																						64.4%	48.0%	150bp	150bp	45%	22.9M	4.7%	73.3%	48.0%	141bp	147bp	27%	22.9M
55_F1_2																						60.7%	49.0%	150bp	150bp	36%	22.9M	4.7%	69.7%	48.0%	141bp	147bp	18%	22.9M
65_F1	0.1	49.2%	0.99	20.4M	44.2%	0.58%	4.8M	40.8M	100.0%	100.0%	0.5%	40.8M	45.6M	45.6M	100.0%	25.9M	20.4M	78.6%	19.1M	73.5%	1.3M
65_F1_1																						59.8%	48.0%	150bp	150bp	36%	26.0M	6.5%	70.2%	48.0%	139bp	147bp	18%	25.9M
65_F1_2																						55.0%	48.0%	150bp	150bp	36%	26.0M	6.5%	65.7%	48.0%	139bp	147bp	18%	25.9M
66_F1	0.1	48.8%	1.00	22.3M	45.6%	0.60%	4.1M	44.5M	100.0%	100.0%	0.3%	44.5M	48.6M	48.6M	100.0%	28.3M	22.3M	78.5%	21.0M	74.0%	1.3M
66_F1_1																						59.8%	48.0%	150bp	150bp	36%	28.4M	4.9%	70.3%	48.0%	141bp	147bp	18%	28.3M
66_F1_2																						55.5%	49.0%	150bp	150bp	36%	28.4M	5.0%	65.5%	48.0%	141bp	147bp	18%	28.3M
69_M3	0.1	50.5%	1.00	18.3M	44.2%	0.56%	3.3M	36.7M	100.0%	100.0%	0.2%	36.7M	40.0M	40.0M	100.0%	22.6M	18.4M	81.2%	17.3M	76.4%	1.1M
69_M3_1																						60.7%	48.0%	150bp	150bp	36%	22.6M	4.7%	69.7%	48.0%	141bp	147bp	18%	22.6M
69_M3_2																						56.3%	49.0%	150bp	150bp	36%	22.6M	4.7%	65.7%	48.0%	141bp	147bp	18%	22.6M
70_M3	0.1	51.4%	1.00	18.0M	43.8%	0.55%	2.9M	36.0M	100.0%	100.0%	0.3%	36.0M	38.9M	38.9M	100.0%	22.5M	18.0M	80.2%	17.1M	75.9%	1.0M
70_M3_1																						60.6%	48.0%	150bp	150bp	36%	22.5M	6.2%	70.1%	49.0%	139bp	147bp	27%	22.5M
70_M3_2																						56.9%	49.0%	150bp	150bp	27%	22.5M	6.3%	66.9%	49.0%	139bp	147bp	9%	22.5M
71_M3	0.1	48.5%	1.01	14.3M	43.6%	0.52%	4.3M	28.6M	100.0%	100.0%	0.8%	28.6M	32.9M	32.9M	100.0%	18.0M	14.3M	79.5%	13.2M	73.5%	1.1M
71_M3_1																						58.4%	47.0%	150bp	150bp	36%	18.0M	5.8%	67.6%	47.0%	139bp	147bp	18%	18.0M
71_M3_2																						54.9%	47.0%	150bp	150bp	36%	18.0M	5.9%	63.8%	47.0%	139bp	147bp	18%	18.0M
73_M3	0.1	48.5%	1.01	15.5M	46.8%	0.57%	2.3M	30.9M	100.0%	100.0%	0.2%	30.9M	33.2M	33.2M	100.0%	19.4M	15.5M	79.5%	14.7M	75.5%	0.8M
73_M3_1																						58.8%	48.0%	150bp	150bp	36%	19.5M	5.9%	68.5%	48.0%	139bp	147bp	18%	19.4M
73_M3_2																						54.9%	48.0%	150bp	150bp	36%	19.5M	6.0%	64.9%	48.0%	139bp	147bp	18%	19.4M
74_M3	0.1	50.4%	1.01	18.7M	43.8%	0.57%	3.8M	37.3M	100.0%	100.0%	0.4%	37.3M	41.1M	41.1M	100.0%	23.8M	18.7M	78.5%	17.5M	73.7%	1.1M
74_M3_1																						60.8%	48.0%	150bp	150bp	36%	23.8M	6.6%	70.3%	48.0%	138bp	147bp	18%	23.8M
74_M3_2																						56.3%	48.0%	150bp	150bp	36%	23.8M	6.6%	66.4%	48.0%	138bp	147bp	18%	23.8M
79_M3	0.1	50.4%	1.00	19.4M	44.4%	0.54%	3.4M	38.9M	100.0%	100.0%	0.3%	38.9M	42.3M	42.3M	100.0%	23.6M	19.5M	82.4%	18.3M	77.7%	1.1M
79_M3_1																						59.5%	49.0%	150bp	150bp	36%	23.6M	4.1%	69.6%	49.0%	142bp	147bp	18%	23.6M
79_M3_2																						57.8%	49.0%	150bp	150bp	27%	23.6M	4.2%	67.5%	49.0%	142bp	147bp	9%	23.6M
83_F3	0.1	49.4%	0.99	21.2M	45.2%	0.59%	3.8M	42.5M	100.0%	100.0%	0.3%	42.5M	46.3M	46.3M	100.0%	26.6M	21.2M	80.0%	20.0M	75.4%	1.2M
83_F3_1																						59.1%	48.0%	150bp	150bp	36%	26.6M	5.4%	69.7%	48.0%	140bp	147bp	18%	26.6M
83_F3_2																						55.7%	49.0%	150bp	150bp	36%	26.6M	5.5%	66.0%	49.0%	140bp	147bp	18%	26.6M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	Custom content: DupRadar	dupInt	Intercept value from DupRadar	`custom_content_dupradar-dupRadar_intercept`
\|\|	Picard: Mark Duplicates	Duplication	Mark Duplicates - Percent Duplication	`picard_mark_duplicates-PERCENT_DUPLICATION`
\|\|	QualiMap: RNASeq	5'-3' bias	5'-3' bias	`qualimap_rnaseq-5_3_bias`
\|\|	QualiMap: RNASeq	M Aligned	Reads Aligned (millions)	`qualimap_rnaseq-reads_aligned`	read_count
\|\|	RSeQC: Bam Stat	Proper Pairs	% Reads mapped in proper pairs	`rseqc_bam_stat-proper_pairs_percent`
\|\|	Samtools: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_stats-error_rate`
\|\|	Samtools: stats	Non-primary	Non-primary alignments (millions)	`samtools_stats-non_primary_alignments`	read_count
\|\|	Samtools: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_stats-reads_mapped`	read_count
\|\|	Samtools: stats	% Mapped	% Mapped reads	`samtools_stats-reads_mapped_percent`
\|\|	Samtools: stats	% Proper pairs	% Properly paired reads	`samtools_stats-reads_properly_paired_percent`
\|\|	Samtools: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_stats-reads_MQ0_percent`
\|\|	Samtools: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_stats-raw_total_sequences`	read_count
\|\|	Samtools: flagstat	Reads	Total reads in the bam file (millions)	`samtools_flagstat-flagstat_total`	read_count
\|\|	Samtools: flagstat	Reads mapped	Reads mapped in the bam file (millions)	`samtools_flagstat-mapped_passed`	read_count
\|\|	Samtools: flagstat	% Reads mapped	% Reads mapped in the bam file	`samtools_flagstat-mapped_passed_pct`
\|\|	STAR	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	STAR	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	STAR	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	STAR	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	STAR	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	STAR	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	FastQC (raw)	Dups	% duplicate reads	`fastqc_raw-percent_duplicates`
\|\|	FastQC (raw)	GC	Average % GC content	`fastqc_raw-percent_gc`
\|\|	FastQC (raw)	Avg len	Average read length	`fastqc_raw-avg_sequence_length`
\|\|	FastQC (raw)	Median len	Median read length	`fastqc_raw-median_sequence_length`
\|\|	FastQC (raw)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw-percent_fails`
\|\|	FastQC (raw)	Seqs	Total sequences (millions)	`fastqc_raw-total_sequences`
\|\|	Cutadapt	Trimmed bases	% total base pairs trimmed	`cutadapt-percent_trimmed`
\|\|	FastQC (trimmed)	Dups	% duplicate reads	`fastqc_trimmed-percent_duplicates`
\|\|	FastQC (trimmed)	GC	Average % GC content	`fastqc_trimmed-percent_gc`
\|\|	FastQC (trimmed)	Avg len	Average read length	`fastqc_trimmed-avg_sequence_length`
\|\|	FastQC (trimmed)	Median len	Median read length	`fastqc_trimmed-median_sequence_length`
\|\|	FastQC (trimmed)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_trimmed-percent_fails`
\|\|	FastQC (trimmed)	Seqs	Total sequences (millions)	`fastqc_trimmed-total_sequences`

Sample status checks

Reports on sample strandedness status, and any failures in trimming or mapping.

Strandedness Checks

Showing ⁰/₃₅ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
101_F3	pass	RSeQC	reverse	reverse	3.1	96.0	1.0
114_F4	pass	RSeQC	reverse	reverse	2.3	97.2	0.5
117_F3	pass	RSeQC	reverse	reverse	2.0	97.6	0.4
119_F3	pass	RSeQC	reverse	reverse	1.8	97.6	0.6
123_F4	pass	RSeQC	reverse	reverse	2.8	96.4	0.9
124_F4	pass	RSeQC	reverse	reverse	3.1	95.9	1.0
131_M4	pass	RSeQC	reverse	reverse	3.2	95.9	0.9
134_F5	pass	RSeQC	reverse	reverse	3.2	95.9	0.9
135_M4	pass	RSeQC	reverse	reverse	2.6	96.6	0.8
139_M4	pass	RSeQC	reverse	reverse	2.5	96.8	0.7
140_M4	pass	RSeQC	reverse	reverse	3.4	95.5	1.1
142_F5	pass	RSeQC	reverse	reverse	2.9	96.4	0.7
144_M4	pass	RSeQC	reverse	reverse	2.7	96.4	0.9
146_M4	pass	RSeQC	reverse	reverse	2.4	97.0	0.6
152_M4	pass	RSeQC	reverse	reverse	2.1	97.2	0.7
154_F5	pass	RSeQC	reverse	reverse	2.6	96.7	0.7
158_F5	pass	RSeQC	reverse	reverse	2.1	97.1	0.8
159_F5	pass	RSeQC	reverse	reverse	2.3	97.2	0.6
173_F5	pass	RSeQC	reverse	reverse	2.1	97.3	0.6
176_F5	pass	RSeQC	reverse	reverse	2.2	97.2	0.6
35_F1	pass	RSeQC	reverse	reverse	2.3	97.1	0.6
36_F1	pass	RSeQC	reverse	reverse	2.5	96.8	0.7
45_M3	pass	RSeQC	reverse	reverse	2.2	97.4	0.4
51_F1	pass	RSeQC	reverse	reverse	2.6	96.6	0.8
54_F1	pass	RSeQC	reverse	reverse	1.8	97.8	0.3
55_F1	pass	RSeQC	reverse	reverse	1.8	97.8	0.4
65_F1	pass	RSeQC	reverse	reverse	2.5	96.7	0.8
66_F1	pass	RSeQC	reverse	reverse	2.7	96.5	0.9
69_M3	pass	RSeQC	reverse	reverse	2.5	96.9	0.7
70_M3	pass	RSeQC	reverse	reverse	2.5	96.8	0.6
71_M3	pass	RSeQC	reverse	reverse	2.1	97.3	0.6
73_M3	pass	RSeQC	reverse	reverse	2.0	97.5	0.5
74_M3	pass	RSeQC	reverse	reverse	2.4	96.9	0.7
79_M3	pass	RSeQC	reverse	reverse	2.6	96.8	0.6
83_F3	pass	RSeQC	reverse	reverse	2.8	96.6	0.7

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fail_strand_check_table_table

Sort	Column	Description	ID
\|\|	Status	Pass if the strandedness inferred by RSeQC matches that provided by the user or predicted by Salmon. Fail otherwise.	`samples_strandedness_check-Status`
\|\|	Strand inference method	Tool used to detect strandedness	`samples_strandedness_check-Strand_inference_method`
\|\|	Provided strandedness	User-provided strandedness	`samples_strandedness_check-Provided_strandedness`
\|\|	Inferred strandedness	Interred strandedness	`samples_strandedness_check-Inferred_strandedness`
\|\|	Sense (%)	Sense (%)	`samples_strandedness_check-Sense`
\|\|	Antisense (%)	Antisense (%)	`samples_strandedness_check-Antisense`
\|\|	Unstranded (%)	Unstranded (%)	`samples_strandedness_check-Unstranded`

FastQC (raw)

This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

All samples have sequences of a single length (150bp)

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
CGTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTTTGGTGATGCA	13	354289	0.0214%
TCACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCAATAAG	11	336476	0.0203%
ACACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCAATAAG	11	349336	0.0211%
CGTCATATCTATAAGTGGACGGCTATCAACTTCAGGAATTAATTTAAAGC	11	303043	0.0183%
AGGGAAGTGTATGAGCCGTGCTTTGTCTTCCAATGGCCCATTAGCAAGAC	9	265838	0.0160%
AGTTGGAATAATCAAAACACTGCAAGAGCCAGCGAGGAATACAAACTCCA	9	255860	0.0154%
CGGGAAATATAAAATGCAGTATGCTGAAGAGAGGGCACAGCATTACACCT	6	154411	0.0093%
ACTCTGTTTGACTCCCGTCTCCGGTGTTTCATTTCAATAATGCAGAAGTG	6	167961	0.0101%
TCTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAA	5	241229	0.0146%
GGTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTTTGGTGATGCA	5	124404	0.0075%
AGTTGAACTCTGCTGACAGCGATGGAGAACAGACGCTGGCCATCTGTGAT	4	159886	0.0096%
ACTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAA	4	201984	0.0122%
TGTTGAACTCTGCTGACAGCGATGGAGAACAGACGCTGGCCATCTGTGAT	3	124555	0.0075%
AGTCGAAACCAAAAACTCCACATTTTTCATTTTAACAGCTATTTGTTCCC	3	73455	0.0044%
AGCAGGATATGTAGATTTTTTTGACAGCTCGACACACAAATAATCTTTTG	3	67835	0.0041%
TGGGAAGTGTATGAGCCGTGCTTTGTCTTCCAATGGCCCATTAGCAAGAC	3	84064	0.0051%
ATGATGATGTAATCAGAGTTACAGAAATCAGGGAAGATTTTGTTGAGCTT	2	53048	0.0032%
AGTGAGACTCCCTTTTTTAAATGGAGCTGGTTTCACTTCTGCATTATTGA	2	49833	0.0030%
AGCCTCTTCTACTTACACATGAGAGTACAGAAAGTTTGCATTAAGCATTA	2	48943	0.0030%
GCTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAA	2	86283	0.0052%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_raw_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_raw-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_raw-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_raw-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Min:

Max:

Created with MultiQC

Cutadapt

Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

Created with MultiQC

Trimmed Sequence Lengths (3')

This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

See the cutadapt documentation for more information on how these numbers are generated.

Created with MultiQC

FastQC (trimmed)

This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
CACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCAATAAGA	35	2266084	0.1369%
GTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTTTGGTGATGCAG	28	1485567	0.0897%
GTCATATCTATAAGTGGACGGCTATCAACTTCAGGAATTAATTTAAAGCT	28	1614682	0.0975%
GGGAAATATAAAATGCAGTATGCTGAAGAGAGGGCACAGCATTACACCTA	28	1411292	0.0852%
GGGAAGTGTATGAGCCGTGCTTTGTCTTCCAATGGCCCATTAGCAAGACT	27	1236885	0.0747%
CTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAAC	26	1444435	0.0873%
GTTGGAATAATCAAAACACTGCAAGAGCCAGCGAGGAATACAAACTCCAG	26	1219295	0.0737%
GTGAGACTCCCTTTTTTAAATGGAGCTGGTTTCACTTCTGCATTATTGAA	25	847708	0.0512%
CTCTGTTTGACTCCCGTCTCCGGTGTTTCATTTCAATAATGCAGAAGTGA	25	1026712	0.0620%
GTCGAAACCAAAAACTCCACATTTTTCATTTTAACAGCTATTTGTTCCCT	24	785907	0.0475%
GTTCATCCCATTCCCACTGCAGCTTCAGAGGAACCAAGCTGGCTATCCCT	23	724424	0.0438%
AAACGACACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCA	23	689156	0.0416%
GCAGGATATGTAGATTTTTTTGACAGCTCGACACACAAATAATCTTTTGG	21	707280	0.0427%
GGAATAATCAAAACACTGCAAGAGCCAGCGAGGAATACAAACTCCAGTTA	21	727159	0.0439%
CTCAACTCTGAGAGCAGCGTGATGGAGTGTATCCAGCTTTGTCACTCCGA	20	580731	0.0351%
GTACTATTAAAATTGAAATTGTGTATCCCCTGATCATTTAAAGTGAATAA	20	653008	0.0394%
TGAGAACGTCTCCTCCCTCTCCATCTTCCTCCTCTCACTTCTGCTGCTGT	19	589344	0.0356%
GTCTTGTTGAGCAGCATGTCAGGAACTCCATCGTAGCGGTCCAGTGTGGT	19	591667	0.0357%
AAAAAATCTAAACCTGGGTCGAAACCAAAAACTCCACATTTTTCATTTTA	18	575206	0.0347%
GTCAGATATCTGTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTT	18	560268	0.0338%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_trimmed_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_trimmed-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_trimmed-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_trimmed-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Min:

Max:

Created with MultiQC

DupRadar

DupRadar provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.URL: bioconductor.org/packages/release/bioc/html/dupRadar.html

Created with MultiQC

Picard

Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

Mark Duplicates

Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
READS_UNMAPPED = UNMAPPED_READS

Created with MultiQC

QualiMap

Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

Genomic origin of reads

Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

Created with MultiQC

Gene Coverage Profile

Mean distribution of coverage depth across the length of all mapped transcripts.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

Created with MultiQC

RSeQC

Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

Read Distribution

Read Distribution calculates how mapped reads are distributed over genome features.

Created with MultiQC

Inner Distance

Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

Created with MultiQC

Read Duplication

read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

Created with MultiQC

Junction Annotation

Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

Created with MultiQC

Junction Saturation

Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

Click a line to see the data side by side (as in the original RSeQC plot).

Created with MultiQC

Infer experiment

Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

Created with MultiQC

Bam Stat

All numbers reported in millions.

Created with MultiQC

Showing ⁰/₃₅ rows and ¹⁴/₁₄ columns.

Sample Name	Total records	QC failed	Duplicates	Non primary hit	Unmapped	Unique	Read-1	Read-2	+ve strand	-ve strand	Non-splice reads	Splice reads	Proper pairs	Different chrom
101_F3	49.1M	0.0M	21.2M	4.0M	0.0M	23.3M	11.6M	11.6M	11.6M	11.6M	14.2M	9.1M	23.2M	0.0M
114_F4	37.9M	0.0M	17.0M	3.6M	0.0M	16.8M	8.4M	8.4M	8.4M	8.4M	10.4M	6.5M	16.8M	0.0M
117_F3	35.8M	0.0M	15.8M	4.1M	0.0M	15.5M	7.8M	7.7M	7.8M	7.7M	9.4M	6.1M	15.5M	0.0M
119_F3	26.2M	0.0M	10.4M	1.6M	0.0M	13.8M	6.9M	6.9M	6.9M	6.9M	8.6M	5.2M	13.8M	0.0M
123_F4	34.8M	0.0M	13.0M	2.2M	0.0M	19.2M	9.6M	9.6M	9.6M	9.6M	12.0M	7.2M	19.2M	0.0M
124_F4	40.6M	0.0M	15.8M	1.9M	0.0M	22.4M	11.2M	11.2M	11.2M	11.2M	14.0M	8.5M	22.4M	0.0M
131_M4	50.7M	0.0M	20.3M	4.3M	0.0M	25.5M	12.8M	12.7M	12.8M	12.8M	15.3M	10.2M	25.5M	0.0M
134_F5	48.1M	0.0M	19.6M	3.9M	0.0M	23.9M	12.0M	12.0M	12.0M	12.0M	14.7M	9.2M	23.9M	0.0M
135_M4	33.6M	0.0M	12.3M	2.6M	0.0M	18.2M	9.1M	9.1M	9.1M	9.1M	11.7M	6.5M	18.2M	0.0M
139_M4	33.4M	0.0M	13.1M	2.0M	0.0M	17.8M	8.9M	8.9M	8.9M	8.9M	11.1M	6.8M	17.8M	0.0M
140_M4	47.4M	0.0M	18.9M	2.6M	0.0M	25.3M	12.6M	12.6M	12.6M	12.6M	15.8M	9.4M	25.3M	0.0M
142_F5	54.7M	0.0M	25.8M	4.3M	0.0M	23.9M	12.0M	12.0M	12.0M	12.0M	14.8M	9.1M	23.9M	0.0M
144_M4	39.2M	0.0M	17.1M	2.4M	0.0M	19.2M	9.6M	9.6M	9.6M	9.6M	12.0M	7.2M	19.2M	0.0M
146_M4	35.8M	0.0M	15.2M	2.2M	0.0M	17.9M	8.9M	8.9M	8.9M	8.9M	10.5M	7.3M	17.9M	0.0M
152_M4	36.7M	0.0M	16.4M	3.0M	0.0M	16.8M	8.4M	8.4M	8.4M	8.4M	10.1M	6.7M	16.8M	0.0M
154_F5	40.8M	0.0M	18.1M	2.9M	0.0M	19.3M	9.6M	9.6M	9.6M	9.6M	11.7M	7.6M	19.3M	0.0M
158_F5	33.2M	0.0M	15.1M	2.4M	0.0M	15.3M	7.7M	7.7M	7.7M	7.7M	9.4M	5.9M	15.3M	0.0M
159_F5	39.7M	0.0M	18.5M	3.0M	0.0M	17.7M	8.9M	8.9M	8.9M	8.9M	10.8M	6.9M	17.7M	0.0M
173_F5	41.2M	0.0M	19.8M	3.9M	0.0M	16.9M	8.5M	8.5M	8.5M	8.5M	10.3M	6.6M	16.9M	0.0M
176_F5	43.4M	0.0M	20.1M	3.8M	0.0M	19.0M	9.5M	9.5M	9.5M	9.5M	11.6M	7.4M	19.0M	0.0M
35_F1	43.2M	0.0M	20.3M	3.8M	0.0M	18.4M	9.2M	9.2M	9.2M	9.2M	11.5M	7.0M	18.4M	0.0M
36_F1	41.3M	0.0M	18.6M	3.4M	0.0M	18.8M	9.4M	9.4M	9.4M	9.4M	11.4M	7.3M	18.8M	0.0M
45_M3	38.3M	0.0M	18.1M	3.0M	0.0M	16.8M	8.4M	8.4M	8.4M	8.4M	10.2M	6.6M	16.8M	0.0M
51_F1	43.0M	0.0M	19.5M	4.3M	0.0M	18.7M	9.4M	9.4M	9.4M	9.4M	11.2M	7.5M	18.7M	0.0M
54_F1	42.9M	0.0M	20.6M	6.0M	0.0M	15.9M	7.9M	7.9M	7.9M	7.9M	9.6M	6.3M	15.9M	0.0M
55_F1	42.0M	0.0M	19.8M	6.2M	0.0M	15.5M	7.8M	7.8M	7.8M	7.8M	9.5M	6.0M	15.5M	0.0M
65_F1	45.6M	0.0M	20.1M	4.8M	0.0M	20.1M	10.1M	10.1M	10.1M	10.1M	12.2M	7.9M	20.1M	0.0M
66_F1	48.6M	0.0M	21.7M	4.1M	0.0M	22.2M	11.1M	11.1M	11.1M	11.1M	13.6M	8.6M	22.2M	0.0M
69_M3	40.0M	0.0M	18.5M	3.3M	0.0M	17.7M	8.9M	8.9M	8.9M	8.9M	10.8M	6.9M	17.7M	0.0M
70_M3	38.9M	0.0M	18.5M	2.9M	0.0M	17.1M	8.5M	8.5M	8.5M	8.5M	10.4M	6.7M	17.1M	0.0M
71_M3	32.9M	0.0M	13.9M	4.3M	0.0M	14.3M	7.2M	7.2M	7.2M	7.2M	8.9M	5.5M	14.3M	0.0M
73_M3	33.2M	0.0M	15.0M	2.3M	0.0M	15.5M	7.8M	7.8M	7.8M	7.8M	9.4M	6.1M	15.5M	0.0M
74_M3	41.1M	0.0M	18.8M	3.8M	0.0M	18.0M	9.0M	9.0M	9.0M	9.0M	11.1M	6.8M	18.0M	0.0M
79_M3	42.3M	0.0M	19.6M	3.4M	0.0M	18.8M	9.4M	9.4M	9.4M	9.4M	11.1M	7.6M	18.8M	0.0M
83_F3	46.3M	0.0M	21.0M	3.8M	0.0M	20.9M	10.5M	10.5M	10.5M	10.5M	12.8M	8.2M	20.9M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: rseqc_bam_stat_table-1

Sort	Column	Description	ID	Scale
\|\|	Total records	Total records	`total_records`	read_count
\|\|	QC failed	QC failed	`qc_failed`	read_count
\|\|	Duplicates	Optical/PCR duplicate	`optical_pcr_duplicate`	read_count
\|\|	Non primary hit	Non primary hit	`non_primary_hits`	read_count
\|\|	Unmapped	Unmapped reads	`unmapped_reads`	read_count
\|\|	Unique	mapq >= mapq_cut (unique)	`mapq_gte_mapq_cut_unique`	read_count
\|\|	Read-1	Read-1	`read_1`	read_count
\|\|	Read-2	Read-2	`read_2`	read_count
\|\|	+ve strand	Reads map to '+'	`reads_map_to_sense`	read_count
\|\|	-ve strand	Reads map to '-'	`reads_map_to_antisense`	read_count
\|\|	Non-splice reads	Non-splice reads	`non_splice_reads`	read_count
\|\|	Splice reads	Splice reads	`splice_reads`	read_count
\|\|	Proper pairs	Reads mapped in proper pairs	`reads_mapped_in_proper_pairs`	read_count
\|\|	Different chrom	Proper-paired reads map to different chrom	`proper_paired_reads_map_to_different_chrom`	read_count

Samtools

Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

Percent mapped

Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

Created with MultiQC

Alignment stats

This module parses the output from samtools stats. All numbers in millions.

Created with MultiQC

Showing ⁰/₃₅ rows and ¹³/₁₃ columns.

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
101_F3	45.1M	45.1M	45.1M	0.0M	0.0M	0.1M	6310.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.8M	1.8M
114_F4	34.4M	34.3M	34.3M	0.0M	0.0M	0.1M	4813.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	16.0M	1.2M
117_F3	31.8M	31.8M	31.8M	0.0M	0.0M	0.2M	4420.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.7M	1.2M
119_F3	24.6M	24.6M	24.6M	10.4M	0.0M	0.1M	3424.1Mb	0.0Mb	1473.5Mb	0.0M	0.0M	11.2M	1.1M
123_F4	32.7M	32.6M	32.6M	0.0M	0.0M	0.1M	4554.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.8M	1.5M
124_F4	38.8M	38.8M	38.8M	15.8M	0.0M	0.0M	5431.4Mb	0.0Mb	2241.7Mb	0.0M	0.0M	17.8M	1.6M
131_M4	46.4M	46.4M	46.4M	20.3M	0.0M	0.2M	6562.9Mb	0.0Mb	2909.2Mb	0.0M	0.0M	21.5M	1.7M
134_F5	44.2M	44.2M	44.2M	19.6M	0.0M	0.2M	6218.2Mb	0.0Mb	2804.5Mb	0.0M	0.0M	20.4M	1.7M
135_M4	31.0M	31.0M	31.0M	0.0M	0.0M	0.1M	4336.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.2M	1.4M
139_M4	31.4M	31.4M	31.4M	0.0M	0.0M	0.0M	4391.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.4M	1.3M
140_M4	44.7M	44.7M	44.7M	18.9M	0.0M	0.1M	6251.8Mb	0.0Mb	2676.6Mb	0.0M	0.0M	20.3M	2.1M
142_F5	50.4M	50.4M	50.4M	25.8M	0.0M	0.1M	7081.2Mb	0.0Mb	3679.8Mb	0.0M	0.0M	23.5M	1.7M
144_M4	36.7M	36.7M	36.7M	0.0M	0.0M	0.1M	5126.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	16.9M	1.5M
146_M4	33.6M	33.6M	33.6M	15.2M	0.0M	0.1M	4761.4Mb	0.0Mb	2192.5Mb	0.0M	0.0M	15.7M	1.0M
152_M4	33.7M	33.7M	33.7M	16.4M	0.0M	0.1M	4708.1Mb	0.0Mb	2320.3Mb	0.0M	0.0M	15.6M	1.3M
154_F5	37.9M	37.9M	37.9M	0.0M	0.0M	0.1M	5400.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.0M	1.0M
158_F5	30.9M	30.8M	30.8M	0.0M	0.0M	0.1M	4318.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.4M	1.0M
159_F5	36.8M	36.8M	36.8M	0.0M	0.0M	0.1M	5189.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	17.3M	1.1M
173_F5	37.2M	37.2M	37.2M	0.0M	0.0M	0.1M	5166.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	17.1M	1.5M
176_F5	39.6M	39.6M	39.6M	20.1M	0.0M	0.1M	5566.7Mb	0.0Mb	2869.5Mb	0.0M	0.0M	18.5M	1.3M
35_F1	39.3M	39.3M	39.3M	20.3M	0.0M	0.1M	5505.2Mb	0.0Mb	2890.0Mb	0.0M	0.0M	17.8M	1.9M
36_F1	37.9M	37.9M	37.9M	18.6M	0.0M	0.1M	5305.0Mb	0.0Mb	2652.0Mb	0.0M	0.0M	17.3M	1.6M
45_M3	35.3M	35.3M	35.3M	18.1M	0.0M	0.1M	4997.7Mb	0.0Mb	2598.5Mb	0.0M	0.0M	16.4M	1.2M
51_F1	38.7M	38.7M	38.7M	0.0M	0.0M	0.2M	5404.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	17.7M	1.6M
54_F1	37.0M	37.0M	37.0M	20.6M	0.0M	0.3M	5218.2Mb	0.0Mb	2957.1Mb	0.0M	0.0M	16.5M	2.0M
55_F1	35.8M	35.8M	35.8M	19.8M	0.0M	0.3M	5042.9Mb	0.0Mb	2834.6Mb	0.0M	0.0M	16.6M	1.3M
65_F1	40.8M	40.8M	40.8M	0.0M	0.0M	0.2M	5661.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.7M	1.7M
66_F1	44.5M	44.5M	44.5M	21.7M	0.0M	0.1M	6265.6Mb	0.0Mb	3106.9Mb	0.0M	0.0M	20.8M	1.5M
69_M3	36.7M	36.7M	36.7M	0.0M	0.0M	0.1M	5185.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	17.2M	1.2M
70_M3	36.0M	36.0M	36.0M	18.5M	0.0M	0.1M	5005.2Mb	0.0Mb	2611.0Mb	0.0M	0.0M	16.5M	1.6M
71_M3	28.6M	28.6M	28.6M	13.9M	0.0M	0.2M	3990.9Mb	0.0Mb	1963.9Mb	0.0M	0.0M	13.1M	1.2M
73_M3	30.9M	30.9M	30.9M	0.0M	0.0M	0.1M	4306.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.2M	1.3M
74_M3	37.3M	37.3M	37.3M	18.8M	0.0M	0.1M	5164.8Mb	0.0Mb	2643.9Mb	0.0M	0.0M	17.0M	1.6M
79_M3	38.9M	38.9M	38.9M	0.0M	0.0M	0.1M	5525.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.3M	1.1M
83_F3	42.5M	42.5M	42.5M	21.0M	0.0M	0.1M	5942.9Mb	0.0Mb	2977.7Mb	0.0M	0.0M	19.5M	1.7M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table-1

Sort	Column	Description	ID	Scale
\|\|	Total sequences	Total sequences	`raw_total_sequences`	read_count
\|\|	Mapped & paired	Paired-end technology bit set + both mates mapped	`reads_mapped_and_paired`	read_count
\|\|	Properly paired	Proper-pair bit set	`reads_properly_paired`	read_count
\|\|	Duplicated	PCR or optical duplicate bit set	`reads_duplicated`	read_count
\|\|	QC Failed	QC Failed	`reads_QC_failed`	read_count
\|\|	Reads MQ0	Reads mapped and MQ=0	`reads_MQ0`	read_count
\|\|	Mapped bases (CIGAR)	Mapped bases (CIGAR)	`bases_mapped__cigar`	base_count
\|\|	Bases Trimmed	Bases Trimmed	`bases_trimmed`	base_count
\|\|	Duplicated bases	Duplicated bases	`bases_duplicated`	base_count
\|\|	Diff chromosomes	Pairs on different chromosomes	`pairs_on_different_chromosomes`	read_count
\|\|	Other orientation	Pairs with other orientation	`pairs_with_other_orientation`	read_count
\|\|	Inward pairs	Inward oriented pairs	`inward_oriented_pairs`	read_count
\|\|	Outward pairs	Outward oriented pairs	`outward_oriented_pairs`	read_count

Flagstat

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁰/₃₅ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
101_F3	49.1M	49.1M	49.1M	4.0M	21.2M	45.1M	45.1M	45.1M	0.0M	0.0M	0.0M
114_F4	37.9M	37.9M	37.9M	3.6M	17.0M	34.4M	34.3M	34.3M	0.0M	0.0M	0.0M
117_F3	35.8M	35.8M	35.8M	4.1M	0.0M	31.8M	31.8M	31.8M	0.0M	0.0M	0.0M
119_F3	26.2M	26.2M	26.2M	1.6M	10.4M	24.6M	24.6M	24.6M	0.0M	0.0M	0.0M
123_F4	34.8M	34.8M	34.8M	2.2M	0.0M	32.7M	32.6M	32.6M	0.0M	0.0M	0.0M
124_F4	40.6M	40.6M	40.6M	1.9M	15.8M	38.8M	38.8M	38.8M	0.0M	0.0M	0.0M
131_M4	50.7M	50.7M	50.7M	4.3M	0.0M	46.4M	46.4M	46.4M	0.0M	0.0M	0.0M
134_F5	48.1M	48.1M	48.1M	3.9M	0.0M	44.2M	44.2M	44.2M	0.0M	0.0M	0.0M
135_M4	33.6M	33.6M	33.6M	2.6M	0.0M	31.0M	31.0M	31.0M	0.0M	0.0M	0.0M
139_M4	33.4M	33.4M	33.4M	2.0M	0.0M	31.4M	31.4M	31.4M	0.0M	0.0M	0.0M
140_M4	47.4M	47.4M	47.4M	2.6M	0.0M	44.7M	44.7M	44.7M	0.0M	0.0M	0.0M
142_F5	54.7M	54.7M	54.7M	4.3M	25.8M	50.4M	50.4M	50.4M	0.0M	0.0M	0.0M
144_M4	39.2M	39.2M	39.2M	2.4M	0.0M	36.7M	36.7M	36.7M	0.0M	0.0M	0.0M
146_M4	35.8M	35.8M	35.8M	2.2M	0.0M	33.6M	33.6M	33.6M	0.0M	0.0M	0.0M
152_M4	36.7M	36.7M	36.7M	3.0M	0.0M	33.7M	33.7M	33.7M	0.0M	0.0M	0.0M
154_F5	40.8M	40.8M	40.8M	2.9M	0.0M	37.9M	37.9M	37.9M	0.0M	0.0M	0.0M
158_F5	33.2M	33.2M	33.2M	2.4M	15.1M	30.9M	30.8M	30.8M	0.0M	0.0M	0.0M
159_F5	39.7M	39.7M	39.7M	3.0M	18.5M	36.8M	36.8M	36.8M	0.0M	0.0M	0.0M
173_F5	41.2M	41.2M	41.2M	3.9M	0.0M	37.2M	37.2M	37.2M	0.0M	0.0M	0.0M
176_F5	43.4M	43.4M	43.4M	3.8M	20.1M	39.6M	39.6M	39.6M	0.0M	0.0M	0.0M
35_F1	43.2M	43.2M	43.2M	3.8M	20.3M	39.3M	39.3M	39.3M	0.0M	0.0M	0.0M
36_F1	41.3M	41.3M	41.3M	3.4M	0.0M	37.9M	37.9M	37.9M	0.0M	0.0M	0.0M
45_M3	38.3M	38.3M	38.3M	3.0M	0.0M	35.3M	35.3M	35.3M	0.0M	0.0M	0.0M
51_F1	43.0M	43.0M	43.0M	4.3M	19.5M	38.7M	38.7M	38.7M	0.0M	0.0M	0.0M
54_F1	42.9M	42.9M	42.9M	6.0M	0.0M	37.0M	37.0M	37.0M	0.0M	0.0M	0.0M
55_F1	42.0M	42.0M	42.0M	6.2M	19.8M	35.8M	35.8M	35.8M	0.0M	0.0M	0.0M
65_F1	45.6M	45.6M	45.6M	4.8M	20.1M	40.8M	40.8M	40.8M	0.0M	0.0M	0.0M
66_F1	48.6M	48.6M	48.6M	4.1M	21.7M	44.5M	44.5M	44.5M	0.0M	0.0M	0.0M
69_M3	40.0M	40.0M	40.0M	3.3M	0.0M	36.7M	36.7M	36.7M	0.0M	0.0M	0.0M
70_M3	38.9M	38.9M	38.9M	2.9M	0.0M	36.0M	36.0M	36.0M	0.0M	0.0M	0.0M
71_M3	32.9M	32.9M	32.9M	4.3M	0.0M	28.6M	28.6M	28.6M	0.0M	0.0M	0.0M
73_M3	33.2M	33.2M	33.2M	2.3M	0.0M	30.9M	30.9M	30.9M	0.0M	0.0M	0.0M
74_M3	41.1M	41.1M	41.1M	3.8M	0.0M	37.3M	37.3M	37.3M	0.0M	0.0M	0.0M
79_M3	42.3M	42.3M	42.3M	3.4M	0.0M	38.9M	38.9M	38.9M	0.0M	0.0M	0.0M
83_F3	46.3M	46.3M	46.3M	3.8M	21.0M	42.5M	42.5M	42.5M	0.0M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-flagstat-dp_table-1

Sort	Column	Description	ID	Scale
\|\|	Total Reads	Total Reads	`flagstat_total`	read_count
\|\|	Total Passed QC	Total Passed QC	`total_passed`	read_count
\|\|	Mapped	Mapped	`mapped_passed`	read_count
\|\|	Secondary Alignments	Secondary Alignments	`secondary_passed`	read_count
\|\|	Duplicates	Duplicates	`duplicates_passed`	read_count
\|\|	Paired in Sequencing	Paired in Sequencing	`paired_in_sequencing_passed`	read_count
\|\|	Properly Paired	Properly Paired	`properly_paired_passed`	read_count
\|\|	Self and mate mapped	Reads with itself and mate mapped	`with_itself_and_mate_mapped_passed`	read_count
\|\|	Singletons	Singletons	`singletons_passed`	read_count
\|\|	Mate mapped to diff chr	Mate mapped to different chromosome	`with_mate_mapped_to_a_different_chr_passed`	read_count
\|\|	Diff chr (mapQ >= 5)	Mate mapped to different chromosome (mapQ >= 5)	`with_mate_mapped_to_a_different_chr_mapQ_5__passed`	read_count

Mapped reads per contig

The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

Created with MultiQC

STAR

Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

Summary Statistics

Summary statistics from the STAR alignment

Showing ⁰/₃₅ rows and ¹⁰/₁₉ columns.

Sample Name	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Avg. read len	Avg. mapped len	Splices	Annotated splices	GT/AG splices	GC/AG splices	AT/AC splices	Non-canonical splices	Mismatch rate	Del rate	Del len	Ins rate	Ins len
101_F3	29.6M	22.6M	76.2%	21.3M	72.0%	1.2M	282.0bp	279.9bp	20.7M	20.7M	20.5M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
114_F4	23.0M	17.2M	74.8%	16.1M	70.1%	1.1M	282.0bp	280.2bp	15.5M	15.5M	15.4M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.4bp
117_F3	20.9M	15.9M	76.0%	14.7M	70.5%	1.2M	280.0bp	278.4bp	14.1M	14.1M	14.0M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.8bp
119_F3	15.9M	12.3M	77.2%	11.8M	74.2%	0.5M	280.0bp	278.7bp	11.0M	11.0M	11.0M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.7bp
123_F4	20.7M	16.3M	78.9%	15.6M	75.6%	0.7M	280.0bp	278.9bp	14.8M	14.8M	14.7M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
124_F4	23.2M	19.4M	83.4%	18.8M	80.8%	0.6M	280.0bp	280.2bp	17.6M	17.6M	17.5M	0.1M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	1.6bp
131_M4	28.9M	23.2M	80.5%	22.1M	76.7%	1.1M	284.0bp	282.5bp	22.3M	22.3M	22.1M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.4bp
134_F5	28.7M	22.1M	77.1%	20.9M	72.9%	1.2M	284.0bp	281.3bp	20.7M	20.7M	20.6M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
135_M4	19.7M	15.5M	78.8%	14.7M	74.9%	0.8M	281.0bp	279.6bp	13.4M	13.4M	13.3M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
139_M4	21.6M	15.7M	72.7%	15.0M	69.6%	0.7M	283.0bp	279.8bp	14.6M	14.6M	14.5M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
140_M4	29.0M	22.4M	77.2%	21.5M	74.4%	0.8M	281.0bp	279.5bp	20.7M	20.6M	20.5M	0.1M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	1.6bp
142_F5	31.3M	25.2M	80.7%	23.8M	76.1%	1.4M	282.0bp	280.8bp	23.6M	23.5M	23.4M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.9bp
144_M4	23.3M	18.4M	78.7%	17.5M	75.2%	0.8M	280.0bp	279.1bp	16.8M	16.8M	16.7M	0.1M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	1.7bp
146_M4	20.7M	16.8M	80.9%	16.0M	77.4%	0.7M	285.0bp	283.7bp	16.2M	16.2M	16.1M	0.1M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	1.5bp
152_M4	20.9M	16.8M	80.5%	15.8M	75.7%	1.0M	281.0bp	279.7bp	15.3M	15.2M	15.1M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.5bp
154_F5	24.3M	19.0M	78.1%	18.1M	74.4%	0.9M	287.0bp	284.6bp	17.9M	17.8M	17.8M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	2.0bp
158_F5	20.0M	15.4M	77.0%	14.6M	72.9%	0.8M	282.0bp	279.9bp	14.0M	14.0M	13.9M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.7bp
159_F5	23.6M	18.4M	77.8%	17.4M	73.6%	1.0M	284.0bp	282.3bp	16.9M	16.8M	16.8M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.7bp
173_F5	23.4M	18.6M	79.4%	17.5M	74.5%	1.2M	279.0bp	277.6bp	16.8M	16.8M	16.7M	0.1M	0.0M	0.0M	0.5%	0.0%	1.8bp	0.0%	1.9bp
176_F5	24.8M	19.8M	80.0%	18.6M	75.2%	1.2M	282.0bp	281.1bp	18.2M	18.2M	18.1M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.7bp
35_F1	25.5M	19.7M	77.0%	18.5M	72.4%	1.2M	282.0bp	279.9bp	18.8M	18.8M	18.7M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
36_F1	25.2M	18.9M	75.3%	17.9M	71.2%	1.0M	283.0bp	280.3bp	18.4M	18.3M	18.3M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.3bp
45_M3	21.8M	17.7M	81.1%	16.7M	76.7%	1.0M	285.0bp	283.1bp	16.8M	16.7M	16.7M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.1%	1.8bp
51_F1	24.5M	19.4M	78.9%	18.1M	74.0%	1.2M	281.0bp	279.2bp	18.2M	18.2M	18.1M	0.1M	0.0M	0.1M	0.5%	0.0%	1.9bp	0.0%	1.6bp
54_F1	23.6M	18.5M	78.3%	16.9M	71.8%	1.5M	285.0bp	282.2bp	19.6M	19.6M	19.5M	0.1M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	1.4bp
55_F1	22.9M	17.9M	78.1%	16.3M	70.9%	1.6M	284.0bp	281.7bp	16.3M	16.2M	16.2M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.4bp
65_F1	25.9M	20.4M	78.6%	19.1M	73.5%	1.3M	279.0bp	277.7bp	18.7M	18.6M	18.5M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.8bp
66_F1	28.3M	22.3M	78.5%	21.0M	74.0%	1.3M	283.0bp	281.5bp	20.2M	20.2M	20.1M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.1%	2.2bp
69_M3	22.6M	18.4M	81.2%	17.3M	76.4%	1.1M	284.0bp	282.5bp	16.7M	16.6M	16.6M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.6bp
70_M3	22.5M	18.0M	80.2%	17.1M	75.9%	1.0M	279.0bp	277.8bp	16.4M	16.3M	16.3M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.5bp
71_M3	18.0M	14.3M	79.5%	13.2M	73.5%	1.1M	280.0bp	279.2bp	12.7M	12.7M	12.7M	0.1M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	2.0bp
73_M3	19.4M	15.5M	79.5%	14.7M	75.5%	0.8M	280.0bp	278.7bp	14.2M	14.2M	14.1M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.1%	2.0bp
74_M3	23.8M	18.7M	78.5%	17.5M	73.7%	1.1M	278.0bp	276.9bp	16.3M	16.2M	16.2M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.1%	2.2bp
79_M3	23.6M	19.5M	82.4%	18.3M	77.7%	1.1M	285.0bp	284.1bp	18.1M	18.1M	18.0M	0.1M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.4bp
83_F3	26.6M	21.2M	80.0%	20.0M	75.4%	1.2M	281.0bp	279.9bp	19.5M	19.5M	19.4M	0.1M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.9bp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: star_summary_table_table

Sort	Column	Description	ID	Scale
\|\|	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	Avg. read len	Average input read length	`star-avg_input_read_length`
\|\|	Avg. mapped len	Average mapped length	`star-avg_mapped_read_length`
\|\|	Splices	Number of splices: Total	`star-num_splices`	read_count
\|\|	Annotated splices	Number of splices: Annotated (sjdb)	`star-num_annotated_splices`	read_count
\|\|	GT/AG splices	Number of splices: GT/AG	`star-num_GTAG_splices`	read_count
\|\|	GC/AG splices	Number of splices: GC/AG	`star-num_GCAG_splices`	read_count
\|\|	AT/AC splices	Number of splices: AT/AC	`star-num_ATAC_splices`	read_count
\|\|	Non-canonical splices	Number of splices: Non-canonical	`star-num_noncanonical_splices`	read_count
\|\|	Mismatch rate	Mismatch rate per base	`star-mismatch_rate`
\|\|	Del rate	Deletion rate per base	`star-deletion_rate`
\|\|	Del len	Deletion average length	`star-deletion_length`
\|\|	Ins rate	Insertion rate per base	`star-insertion_rate`
\|\|	Ins len	Insertion average length	`star-insertion_length`

Alignment Scores

Created with MultiQC

Sample relationships

Plots interrogating sample relationships, based on final count matrices.

STAR_SALMON DESeq2 sample similarity

Min:

Max:

Created with MultiQC

STAR_SALMON DESeq2 PCA plot

Created with MultiQC

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
BEDTOOLS_GENOMECOV_FW	bedtools	`2.31.1`
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DESEQ2_QC_STAR_SALMON	bioconductor-deseq2	`1.28.0`
	r-base	`4.0.3`
DupRadar	bioconductor-dupradar	`1.32.0`
FASTQC	fastqc	`0.12.1`
GFFREAD	gffread	`0.12.7`
GTF2BED	perl	`5.26.2`
GTF_FILTER	python	`3.9.5`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
PICARD_MARKDUPLICATES	picard	`3.1.1`
QUALIMAP_RNASEQ	qualimap	`2.3`
RSEQC_BAMSTAT	rseqc	`5.0.2`
RSEQC_INFEREXPERIMENT	rseqc	`5.0.2`
RSEQC_INNERDISTANCE	rseqc	`5.0.2`
RSEQC_JUNCTIONANNOTATION	rseqc	`5.0.2`
RSEQC_JUNCTIONSATURATION	rseqc	`5.0.2`
RSEQC_READDISTRIBUTION	rseqc	`5.0.2`
RSEQC_READDUPLICATION	rseqc	`5.0.2`
SALMON_QUANT	salmon	`1.10.3`
SAMTOOLS_FLAGSTAT	samtools	`1.21`
SAMTOOLS_IDXSTATS	samtools	`1.21`
SAMTOOLS_INDEX	samtools	`1.21`
SAMTOOLS_SORT	samtools	`1.21`
SAMTOOLS_STATS	samtools	`1.21`
SE_GENE	bioconductor-summarizedexperiment	`1.32.0`
STAR_ALIGN	gawk	`5.1.0`
	samtools	`1.2`
	star	`2.7.11b`
STAR_GENOMEGENERATE	gawk	`5.1.0`
	samtools	`1.2`
	star	`2.7.11b`
STRINGTIE_STRINGTIE	stringtie	`2.2.3`
TRIMGALORE	cutadapt	`4.9`
	trimgalore	`0.6.10`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
UCSC_BEDCLIP	ucsc	`377`
UCSC_BEDGRAPHTOBIGWIG	ucsc	`469`
Workflow	Nextflow	`24.04.4`
	nf-core/rnaseq	`v3.17.0-g00f924c`

nf-core/rnaseq Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

Methods

Data was processed using nf-core/rnaseq v3.17.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v24.04.4 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq --input samplesheets/pit_samplesheet.csv --outdir output --fasta ChrAur2_renamed_fixed.fasta --gff ChrAurV1_fixed.gff '--extra_trimgalore_args=--clip_r1=1 --clip_r2=1' --skip_biotype_qc -profile singularity

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/rnaseq Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

Input/output options

input: samplesheets/pit_samplesheet.csv
outdir: output

Reference genome options

fasta: ChrAur2_renamed_fixed.fasta
gff: ChrAurV1_fixed.gff

Read trimming options

extra_trimgalore_args: --clip_r1=1 --clip_r2=1

Alignment options

min_mapped_reads: 5

Process skipping options

skip_biotype_qc: true

Core Nextflow options

configFiles: N/A
containerEngine: singularity
launchDir: /powerplant/workspace/cfnsjm/my_files/C_auratus/transcriptomics/rnaseq
profile: singularity
projectDir: /workspace/cfnsjm/.nextflow/assets/nf-core/rnaseq
revision: master
runName: thirsty_stone
userName: cfnsjm
workDir: /powerplant/workspace/cfnsjm/my_files/C_auratus/transcriptomics/rnaseq/work

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Software Versions

nf-core/rnaseq Methods Description

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